Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker

Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker

Health firm Populytics tracks and analyzes patient data, and makes care suggestions based on that data.

(Photo by National Cancer Institute (left) and Andrew Leu on Unsplash)



The diabetic patient hit the danger zone.

Ideally, blood sugar, measured by an A1C test, rests at 5.9 or less. A 7 is elevated, according to the Diabetes Council. Over 10, and you're into the extreme danger zone, at risk of every diabetic crisis from kidney failure to blindness.

In three months of working with a case manager, Jen's blood sugar had dropped to 7.2, a much safer range.

This patient's A1C was 10. Let's call her Jen for the sake of this story. (Although the facts of her case are real, the patient's actual name wasn't released due to privacy laws.).

Jen happens to live in Pennsylvania's Lehigh Valley, home of the nonprofit Lehigh Valley Health Network, which has eight hospital campuses and various clinics and other services. This network has invested more than $1 billion in IT infrastructure and founded Populytics, a spin-off firm that tracks and analyzes patient data, and makes care suggestions based on that data.

When Jen left the doctor's office, the Populytics data machine started churning, analyzing her data compared to a wealth of information about future likely hospital visits if she did not comply with recommendations, as well as the potential positive impacts of outreach and early intervention.

About a month after Jen received the dangerous blood test results, a community outreach specialist with psychological training called her. She was on a list generated by Populytics of follow-up patients to contact.

"It's a very gentle conversation," says Cathryn Kelly, who manages a care coordination team at Populytics. "The case manager provides them understanding and support and coaching." The goal, in this case, was small behavioral changes that would actually stick, like dietary ones.

In three months of working with a case manager, Jen's blood sugar had dropped to 7.2, a much safer range. The odds of her cycling back to the hospital ER or veering into kidney failure, or worse, had dropped significantly.

While the health network is extremely localized to one area of one state, using data to inform precise medical decision-making appears to be the wave of the future, says Ann Mongovern, the associate director of Health Care Ethics at the Markkula Center for Applied Ethics at Santa Clara University in California.

"Many hospitals and hospital systems don't yet try to do this at all, which is striking given where we're at in terms of our general technical ability in this society," Mongovern says.

How It Happened

While many hospitals make money by filling beds, the Lehigh Valley Health Network, as a nonprofit, accepts many patients on Medicaid and other government insurances that don't cover some of the costs of a hospitalization. The area's population is both poorer and older than national averages, according to the U.S. Census data, meaning more people with higher medical needs that may not have the support to care for themselves. They end up in the ER, or worse, again and again.

In the early 2000s, LVHN CEO Dr. Brian Nester started wondering if his health network could develop a way to predict who is most likely to land themselves a pricey ICU stay -- and offer support before those people end up needing serious care.

Embracing data use in such specific ways also brings up issues of data security and patient safety.

"There was an early understanding, even if you go back to the (federal) balanced budget act of 1997, that we were just kicking the can down the road to having a functional financial model to deliver healthcare to everyone with a reasonable price," Nester says. "We've got a lot of people living longer without more of an investment in the healthcare trust."

Popultyics, founded in 2013, was the result of years of planning and agonizing over those population numbers and cost concerns.

"We looked at our own health plan," Nester says. Out of all the employees and dependants on the LVHN's own insurance network, "roughly 1.5 percent of our 25,000 people — under 400 people — drove $30 million of our $130 million on insurance costs -- about 25 percent."

"You don't have to boil the ocean to take cost out of the system," he says. "You just have to focus on that 1.5%."

Take Jen, the diabetic patient. High blood sugar can lead to kidney failure, which can mean weekly expensive dialysis for 20 years. Investing in the data and staff to reach patients, he says, is "pennies compared to $100 bills."

For most doctors, "there's no awareness for providers to know who they should be seeing vs. who they are seeing. There's no incentive, because the incentive is to see as many patients as you can," he says.

To change that, first the LVHN invested in the popular medical management system, Epic. Then, they negotiated with the top 18 insurance companies that cover patients in the region to allow access to their patient care data, which means they have reams of patient history to feed the analytics machine in order to make predictions about outcomes. Nester admits not every hospital could do that -- with 52 percent of the market share, LVHN had a very strong negotiating position.

Third party services take that data and churn out analytics that feeds models and care management plans. All identifying information is stripped from the data.

"We can do predictive modeling in patients," says Populytics President and CEO Gregory Kile. "We can identify care gaps. Those care gaps are noted as alerts when the patient presents at the office."

Kile uses himself as a hypothetical patient.

"I pull up Gregory Kile, and boom, I see a flag or an alert. I see he hasn't been in for his last blood test. There is a care gap there we need to complete."

"There's just so much more you can do with that information," he says, envisioning a future where follow-up for, say, knee replacement surgery and outcomes could be tracked, and either validated or changed.

Ethical Issues at the Forefront

Of course, embracing data use in such specific ways also brings up issues of security and patient safety. For example, says medical ethicist Mongovern, there are many touchpoints where breaches could occur. The public has a growing awareness of how data used to personalize their experiences, such as social media analytics, can also be monetized and sold in ways that benefit a company, but not the user. That's not to say data supporting medical decisions is a bad thing, she says, just one with potential for public distrust if not handled thoughtfully.

"You're going to need to do this to stay competitive," she says. "But there's obviously big challenges, not the least of which is patient trust."

So far, a majority of the patients targeted – 62 percent -- appear to embrace the effort.

Among the ways the LVHN uses the data is monthly reports they call registries, which include patients who have just come in contact with the health network, either through the hospital or a doctor that works with them. The community outreach team members at Populytics take the names from the list, pull their records, and start calling. So far, a majority of the patients targeted – 62 percent -- appear to embrace the effort.

Says Nester: "Most of these are vulnerable people who are thrilled to have someone care about them. So they engage, and when a person engages in their care, they take their insulin shots. It's not rocket science. The rocket science is in identifying who the people are — the delivery of care is easy."

Anne Miller
Anne Miller is an editor and writer based in Brooklyn who is particularly curious about how technology impacts our daily lives. Her byline has appeared in the New York Times, the Washington Post, the Wall Street Journal and Slate, and she's a regular contributor to Dell Perspectives — when she's not managing editorial projects for Fortune 500 firms. She holds a master's degree in Human-Computer Interaction from the Rensselaer Polytechnic Institute.
A 3D-printed tongue reveals why chocolate tastes so good—and how to reduce its fat

Researchers are looking to engineer chocolate with less oil, which could reduce some of its detriments to health.

Adobe Stock

Creamy milk with velvety texture. Dark with sprinkles of sea salt. Crunchy hazelnut-studded chunks. Chocolate is a treat that appeals to billions of people worldwide, no matter the age. And it’s not only the taste, but the feel of a chocolate morsel slowly melting in our mouths—the smoothness and slipperiness—that’s part of the overwhelming satisfaction. Why is it so enjoyable?

That’s what an interdisciplinary research team of chocolate lovers from the University of Leeds School of Food Science and Nutrition and School of Mechanical Engineering in the U.K. resolved to study in 2021. They wanted to know, “What is making chocolate that desirable?” says Siavash Soltanahmadi, one of the lead authors of a new study about chocolates hedonistic quality.

Besides addressing the researchers’ general curiosity, their answers might help chocolate manufacturers make the delicacy even more enjoyable and potentially healthier. After all, chocolate is a billion-dollar industry. Revenue from chocolate sales, whether milk or dark, is forecasted to grow 13 percent by 2027 in the U.K. In the U.S., chocolate and candy sales increased by 11 percent from 2020 to 2021, on track to reach $44.9 billion by 2026. Figuring out how chocolate affects the human palate could up the ante even more.

Keep Reading Keep Reading
Cari Shane
Cari Shane is a freelance journalist (and Airbnb Superhost). Originally from Manhattan, Shane lives carless in Washington, DC and writes on a variety of subjects for a wide array of media outlets including, Scientific American, National Geographic, Discover, Business Insider, Fast Company, Fortune and Fodor’s.
Scientists redesign bacteria to tackle the antibiotic resistance crisis

Probiotic bacteria can be engineered to fight antibiotic-resistant superbugs by releasing chemicals that kill them.

Adobe stock

In 1945, almost two decades after Alexander Fleming discovered penicillin, he warned that as antibiotics use grows, they may lose their efficiency. He was prescient—the first case of penicillin resistance was reported two years later. Back then, not many people paid attention to Fleming’s warning. After all, the “golden era” of the antibiotics age had just began. By the 1950s, three new antibiotics derived from soil bacteria — streptomycin, chloramphenicol, and tetracycline — could cure infectious diseases like tuberculosis, cholera, meningitis and typhoid fever, among others.

Today, these antibiotics and many of their successors developed through the 1980s are gradually losing their effectiveness. The extensive overuse and misuse of antibiotics led to the rise of drug resistance. The livestock sector buys around 80 percent of all antibiotics sold in the U.S. every year. Farmers feed cows and chickens low doses of antibiotics to prevent infections and fatten up the animals, which eventually causes resistant bacterial strains to evolve. If manure from cattle is used on fields, the soil and vegetables can get contaminated with antibiotic-resistant bacteria. Another major factor is doctors overprescribing antibiotics to humans, particularly in low-income countries. Between 2000 to 2018, the global rates of human antibiotic consumption shot up by 46 percent.

Keep Reading Keep Reading
Anuradha Varanasi
Anuradha Varanasi is a freelance science journalist based in Mumbai, India. She has an MA in Science Journalism from Columbia University in the City of New York. Her stories on environmental health, biomedical research, and climate change have been published in Forbes, UnDark, Popular Science, and Inverse. You can follow her on Twitter @AnuradhaVaranas